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The article proposes a convex distributed robust model predictive control algorithm for collision avoidance in multi‐agent systems with additive disturbances, which utilizes the concept of tube model predictive control to address the disturbances. To tackle the coupled collision avoidance constraints, each agent integrates assumed nominal position and input trajectories from its neighbors, rather than relying on actual ones. Compatibility constraints are then designed using the normal vector of the constructed separating hyperplanes for collision avoidance and the residual collision avoidance margin of the optimal solutions at the last time step to restrict deviations between assumed and actual trajectories and ensure consistency among the agents. The nonconvex collision and obstacle avoidance constraints are convexified using the concept of safe sets, transforming them into time‐varying closed polyhedral constraints while accounting for the impact of disturbances. Particularly, the residual collision avoidance margin is incorporated into the construction of safe sets for the satisfaction of coupled collision avoidance constraints. Consequently, the original collision avoidance optimal control problems can be efficiently and simultaneously solved for all agents using standard quadratic programming techniques. Under the design of local terminal constraint sets for each agent, the proposed algorithm ensures a rigorous analysis of robust constraint satisfaction for collision avoidance constraints, as well as an assessment of recursive feasibility and closed‐loop stability. The effectiveness of the algorithm is demonstrated through an illustrative example, and simulation results validate its ability to successfully achieve collision and obstacle avoidance even in the presence of disturbances. [ABSTRACT FROM AUTHOR]

This paper proposes a locally convexified rigid tube model predictive control for obstacle avoidance for linear, discrete‐time, systems subject to additive, bounded disturbances and state and control constraints. The inherently nonconvex obstacle avoidance constraints are locally convexified and converted into closed polyhedral constraints by deploying recent notions of safe sets and tubes. The proposed method leverages these recent notions of safe sets and tubes to reformulate the original nonconvex optimization problem for obstacle avoidance within the context of disturbances into a computationally highly efficient, strictly convex quadratic programming problem. In order to analyze the separation between a predicted state set and each obstacle avoidance constraint set, we employ the Euclidean distance between these two sets and the radius of the Chebyshev ball of their intersection. In the strict separation case, the separating hyperplanes are constructed by utilizing the separation theorem for convex sets, while in the weak separation case, the construction of separating hyperplanes is achieved through the use of the separation theorem and the properties of convex cones. The proposed method provides guarantees of strong system theoretic properties such as robust recursive feasibility, robust positive invariance and robust stability of the controlled uncertain system. A simulation example is also provided to numerically verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Metastasis-associated 1 (MTA1), a subunit of the nucleosome remodeling and histone deacetylation (NuRD) corepressor complex, was reported to be expressed in the cytoplasm of skeletal muscles. However, the exact subcellular localization and the functional implications of MTA1 in skeletal muscles have not been examined. This study aims to demonstrate the subcellular localization of MTA1 in skeletal muscles and reveal its possible roles in skeletal muscle pathogenesis. Striated muscles (skeletal and cardiac) from C57BL/6 mice of 4-5 weeks were collected to examine the expression of MTA1 by Western blotting and immunohistochemistry. Immunofluorescence and immunoelectron microscopy were performed for MTA1, α-actinin (a Z-disc marker protein), and SMN (survival of motor neuron) proteins. Gene Expression Omnibus (GEO) data sets were analyzed using the GEO2R online tool to explore the functional implications of MTA1 in skeletal muscles. MTA1 expression was detected by Western blotting and immunohistochemistry in skeletal and cardiac muscles. Subcellular localization of MTA1 was found in the Z-disc of sarcomeres, where α-actinin and SMN were expressed. Data mining of GEO profiles suggested that MTA1 dysregulation is associated with multiple skeletal muscle defects, such as Duchenne muscular dystrophy, Emery-Dreifuss muscular dystrophy, nemaline myopathy, and dermatomyositis. The GEO analysis also showed that MTA1 expression gradually decreased with age in mouse skeletal muscle precursor cells. The subcellular localization of MTA1 in sarcomeres of skeletal muscles implies its biological roles in sarcomere structures and its possible contribution to skeletal muscle pathology., (© 2024 Wiley Periodicals LLC.)